Attraction and diffusion in nature-inspired optimization algorithms

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research

Advances of swarm intelligent systems in gene expression data classification

The step forward in the development of microarray technology of gene expression has created new opportunities in further exploration of living systems, source of disease and drug development and cancer biology. In the analysis of gene expression profiles, the number of tissue samples with genes expression levels available is usually small compared with the number of genes. This can lead either to possible overfitting and dimensionality curse or even to a complete failure in analysis of microarray data. So, the dramatic increase in genomic data volumes make it a challenging task to select genes that are really indicative of the tissue classification a key step to accurately pick out the information from such microarrays. On the other hand, in the last decades, swarm intelligent systems have gained much attention and wide applications in different fields such as solving the gene expression data classification problem. These algorithms are efficient in dealing with optimization issues, and they are also relatively simple to implement with the ability to fast converge to a reasonably good solution. They engage probabilistic rules instead of deterministic ones and require neither derivatives of cost function. In this paper, a hybrid algorithm based on swarm intelligence systems is utilized to classify gene expression dat

Accelerated Particle Swarm for Optimum Design of Frame Structures

Accelerated particle swarm optimization (APSO) is developed for finding optimum design of frame structures. APSO shows some extra advantages in convergence for global search. The modifications on standard PSO effectively accelerate the convergence rate of the algorithm and improve the performance of the algorithm in finding better optimum solutions. The performance of the APSO algorithm is also validated by solving two frame structure problems

An Objective-Based Design Approach of Retaining Walls Using Cuckoo Search Algorithm

In the past two decades, there has been ongoing trend to design retaining walls in an optimal way rather than the conventional trial and-error approach. In this study, reinforced concrete cantilever retaining wall is optimized using the Cuckoo Search (CS) algorithm, a metaheuristic swarm-based method that imitates the reproductive behavior of cuckoo birds. To obtain the optimal solution, design requirements are expressed as constraints to overcome violated solutions. Together with a mathematical definition of the objective function, three constraint groups are used to represent the geotechnical, structural, and geometrical design considerations. In addition, an objective-based design approach is introduced to optimize the cost and weight objective functions simultaneously. The performance of theCS is proved through its application on cantilever retaining walls, where two numerical examples are solved in terms of both cost and the weight of the walls. The results indicate that the CS is be a viable solution for the optimum design of retaining walls